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27th Southern African Universities Power Engineering Conference (SAUPEC) / 11th Robotics and Mechatronics Conference of South Africa (RobMech) / 29th Annual Symposium of Pattern-Recognition-Association-of-South-Africa (PRASA)

作者： Onaolapo, A. K. Akindeji, K. T. Durban Univ Technol Dept Elect Power Engn POB 1334 ZA-4000 Durban South Africa

ISBN: (纸本)9781728103693

Occurrence of faults in power systems is unavoidable but their timely recognition and location enhances the reliability and security of supply;thereby resulting in economic gain to consumers and power utility alike. Distribution Network (DN) is made smarter by the introduction of sensors and computers into the system. In this paper, detection and classification of faults in DN using Artificial Neural Network (ANN) is emphasized. This is achieved through the employment of back propagation algorithm (BPA) of the Feed Forward Neural Network (FFNN) using three phase voltages and currents as inputs. The simulations were carried out using the MATLAB (R) 2017a. ANN with various hidden layers were analyzed and the results authenticate the effectiveness of the method.

关键词： Artificial Neural Networks Faults Distribution Network Sensors back propagation algorithm

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Neural Network Solution of the Circular Loop Antenna Radiation Problem

Neural Network Solution of the Circular Loop Antenna Radiati...

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20th Telecommunications Forum

作者： Kapetanakis, Theodoros N. Vardiambasis, Ioannis O. Liodakis, George S. Maras, Andreas Technol Educ Inst TEI Crete Branch Chania Dept Elect Broadband Commun & Elect Applicat Lab Romanou 3 Chalepa Chania Greece Univ Peloponnese Dept Telecommunicat Sci & Technol Arcadia 22100 Greece

ISBN: (纸本)9781467329842;9781467329835

The circular cylindrical antenna is a simple, inexpensive, versatile, and very popular antenna type, which has received much attention due its wide range of applications. The exact values of the radiated near and far electromagnetic fields have recently been analytically evaluated, in terms of complex series involving Legendre functions of the second kind and half-integral order. The inverse problem of determining the loop antenna parameters (radius and current) causing specific field levels at one or more points of interest is even more complex. In order to find the solution, avoiding the associated lengthy and time-demanding mathematical analysis, we apply artificial neural network modeling. The proposed models consist of a feed-forward back-propagation and a radial basis neural network trained with theoretical data. The results obtained are found to be in perfect agreement with the exact theoretical data.

关键词： Antenna radiation back propagation algorithm circular loop antenna inverse radiation problem neural networks Legendre functions radial basis function

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Voice Controlled Urdu Interface using Isolated and Continuous Speech Recognizer

Voice Controlled Urdu Interface using Isolated and Continuou...

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15th IEEE International Multitopic Conference (INMIC)

作者： Ali, Saira Iqbal, Sidra Saeed, Imran Int Islamic Univ Islamabad Dept Comp Sci & Software Engn Islamabad Pakistan

ISBN: (纸本)9781467322522

We propose multilayer feed forward Neural Network for Urdu isolated words and Urdu continuous speech recognition. Our application not only recognizes the voice commands but also converts continuous speech into Urdu Fonts. Our work is based on converting analog signal to digital signals, removing silence and noise through filters, extracting 39 Mel Frequency Cepstral Coefficients(MFCCs) from the speech signal, applying k-mean algorithm and using them as an input to multi-layer perceptron neural networks. Urdu text stores in"Jameel Noori Nastaleeq" fonts. Isolated words gain accuracy of 95.6% in speaker dependent mode while 30-40% in speaker independent mode. Continuous speech also shows a good accuracy of 96.5% in speaker dependent mode and 30-40% for speaker independent mode. Current work is being extended to improve accuracy for speaker independent system by improving dataset and feature set.

关键词： component Speaker Dependent System Speaker Independent System acoustic modeling back propagation algorithm

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Feature Extraction and Recognition of Bengali Word Using Gabor Filter and Artificial Neural Network

Feature Extraction and Recognition of Bengali Word Using Gab...

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International Conference on Infirmation Systems for Indian Languages (ICISIL 2011)

作者： Nandy (Pal), Mahua Majumdar, Sumit MCKV Inst Engn Dept CSE Howrah W Bengal India

ISBN: (纸本)9783642194023

Character recognition is an emerging area of research in the field of image processing and pattern recognition. The objective here is to generate a Bengali dictionary and develop an artificial neural network based technique for matching an input word with the dictionary word. The technique uses the features of the words as a whole rather than the features of each character. For feature extraction purpose, we have used 2D Gabor filter. For the dictionary words, it shows 93.67% accuracy in matching and for non-dictionary words, it shows 83% accuracy in non-matching. The overall accuracy of the system becomes 91%.

关键词： Gabor filter iLeap binarization normalization orientation matrix back propagation algorithm

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Over-The-Air Calibration of A Phase Shifter Network

Over-The-Air Calibration of A Phase Shifter Network

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Zhang, Wei Jiang, Yi Fudan Univ Dept Commun Sci & Engn Shanghai Peoples R China

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

Phase shifter networks (PSN) are now widely used in multi-input multi-output (MIMO) systems for its low cost and analog signal processing capability. In practice, the phase shifters may be subject to phase deviations, which needs to be properly estimated and calibrated. This paper proposes a novel over-the-air (OTA) approach to estimate the deviations of the phase shifters at each gear. We formulate the PSN calibration model by the so-termed quasi-neural network (quasi-NN). In training the quasi-NN using the back propagation (BP) algorithm, the phase deviations are automatically estimated. The simulation results verify the effectiveness of the proposed algorithm by showing that the root mean square errors (RMSEs) of the phase estimates are close to the Cramer Rao Bounds (CRBs).

关键词： MIMO quasi-neural network phased array calibration back propagation algorithm

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Development of a hybrid neural network system for prediction of process parameters in injection moulding

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2001年第1-3期118卷 110-116页

作者： Yarlagadda, PKDV Khong, CAT Queensland Univ Technol Sch Mech Mfg & Med Engn Brisbane Qld Australia

In this paper, the attempts made by the authors to develop an artificial neural network system for prediction of injection moulding process parameters is presented. In this work, attempts have been made to determine the process parameters that could affect injection moulding process based on governing equations of the filling process. Focus is then directed to parameters that require the use of trial and error methods or other complex software to determine the process parameters. The two parameters that are predicted from the developed network are injection time and injection pressure. In this work, the training data are generated by simulation using C-MOLD flow simulation software. A total of 114 data were collected out of which 94 were used to train the network using MATLAB and the remaining 20 for testing the network. Two algorithms are used during the training phase, namely the error-back-propagation algorithm and the Levenberg-Marquardt approximation algorithm. Results showed that the latter algorithm is more suitable for this a plication since the Leverberg's algorithm converged rapidly with lesser training cycles when compared to the error-back-propagation algorithm. The accuracy of the developed network has been tested by predicting the injection pressure and injection time for few engineering components and found that the overall error is 0.93% with a deviation of 3.93%. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： artificial intelligence hybrid neural networks injection moulding back propagation algorithm

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Infrared Face Recognition Using Neural Networks and HOG-SVM 3

Infrared Face Recognition Using Neural Networks and HOG-SVM

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3rd International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Cherifi, Dalila Kaddari, Redouane Zair, Hamza Nait-Ali, Amine Univ Boumerdes Inst Elect & Elect Engn Boumerdes Algeria Paris Est Creteil UPEC LiSSi EA 3956 Creteil France

ISBN: (纸本)9781728135786

With the rapid development of computer vision applications, infrared face recognition is receiving much attention because of miniscule sensitivity to the face variations due to illumination changes. The infrared image is merely a gray level image but the recognition is interestingly good enough to look at. In this article we present an infrared face recognition system where we used two methods and two databases to train and test our system. We used the Histogram of Gradient (HOG) algorithm along with the Support Vector Machine (SVM) classifier as a first method. In the second method, we implemented the backpropagation algorithm. Although the results of the HOG-SVM were very promising and we were able to get a better results compared with a previous work, neural network gave us a perfect results by using the same number of training examples.

关键词： Face Recognition HOG-SVM back propagation algorithm Biometric

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An APSO Optimized BP Neural Network

An APSO Optimized BP Neural Network

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IEEE International Conference on Electronics, Communications and Control (ICECC)

作者： Mo Lihong Huaiyin Inst Technol Fac Elect & Elect Engn Huaian Peoples R China

ISBN: (纸本)9781457703218

In this paper, the limitations of conventional BP algorithm was analyzed, and to fasten the learning velocity of neural network and enhance its generalization capability, the APSO (adaptive particle swarm optimization) algorithm was introduced into BP network for the optimization of its weights and thresholds. To overcome its 'early maturity', the variance operation was made on particles owing bigger fitness values according to certain probability, and the inertia weights were dynamic changed during the iteration process. The algorithm was simulated on a nonlinear function regression problem, which shows that the improved PSO-BP algorithm has faster learning velocity and better generalization ability than conventional BP network. The simulation results prove that the APSO-BP algorithm can get over the limitations of the conventional BP network and is superior to it.

关键词： back propagation algorithm particle swarm optimization generalization capability Simulation

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A Novel Approach of Input Variable Selection for ANN Based Load Forecasting

A Novel Approach of Input Variable Selection for ANN Based L...

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Joint International Conference on Power Systems Technology (POWERCON)/IEEE Power India Conference

作者： Shrivastava, Vivek Misra, R. B. Indian Inst Technol Ctr Reliabil Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9781424417636

The forecasting of electricity demand has become one of the major research fields in electrical engineering. The supply industry requires forecasts with lead times, which range from the short term (a few minutes, hours, or days ahead) to the long term (up to 20 years ahead). The major priority for an electrical power utility is to provide uninterrupted power supply to its customers. Long term peak load forecasting plays an important role in electrical power systems in terms of policy planning and budget allocation. This paper presents a peak load forecasting model using Artificial Neural Networks (ANN). The approach in the paper is based on multi-layered back-propagation feed forward neural network. A case study is performed using the proposed method of peak load data of the Grid Corporation of Orrissa (GRIDCO), India which maintain high quality, reliable, historical data.

关键词： back propagation algorithm Load Forecasting Multi-layered Neural Model Neural Network

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High Impedance Arcing Fault Detection in MV Networks Using Discrete Wavelet Transform and Artificial Neural Networks

High Impedance Arcing Fault Detection in MV Networks Using D...

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International Conference on Green Technologies (ICGT)

作者： Vijayachandran, Gayathri Mathew, Bobin. K. Amal Jyothi Coll Engn Dept Elect & Elect Kottayam Kerala India

ISBN: (纸本)9781467326360;9781467326353

Arcing faults in transmission networks are caused when a current carrying conductor makes an unwanted electrical contact with ground or is temporarily short circuited with another current carrying conductor through a high impedance medium. High impedance arcing faults restricts the flow of current below the detection level of the protective devices and hence cannot be detected by conventional relays. In this paper a new method is proposed for the detection of arcing faults due to leaning trees in medium voltage (MV) networks. Firstly, an arc model is developed in order to reproduce the fault circumstances. Then based on a fault detection algorithm the fault features are extracted using a signal processing technique called Discrete Wavelet Transform (DWT). The proposed algorithm is implemented in a simple MV network to identify the faulty phase and in a feeder network to identify both the faulty phase and feeder. Further the results obtained using DWT is validated with the help of Artificial Neural Networks (ANN). The results obtained above validate the effectiveness of the proposed methodology.

关键词： High impedance fault Arc model Universal Arc representation Discrete Wavelet Transform Absolute sum Artificial Neural Networks back propagation algorithm

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